Examining the extremal dependence structure of precipitation in Norway

2020 ◽  
Author(s):  
Silius Mortensønn Vandeskog ◽  
Sara Martino
Keyword(s):  
Extreme Precipitation ◽  
Stable Process ◽  
Value Theory ◽  
Distribution Functions ◽  
Asymptotic Independence ◽  
Ensemble Prediction ◽  
Dependence Structure ◽  
Ensemble Prediction System ◽  
Extremal Dependence ◽  
Spatial Extremes

<p>Extreme precipitation can lead to great floods and landslides and cause severe damage and economical losses. It is therefore of great importance that we manage to assess the risk of future extremes. Furthermore, natural hazards are spatiotemporal phenomena that require extensive modelling in both space and time. Extreme value theory (EVT) can be used for statistical modelling of spatial extremes, such as extreme precipitation over a catchment. An important concept when modelling a natural hazard is the degree of extremal dependence for the given phenomenon. Extremal dependence describes the possibility of multiple extremes occurring at the same time. For the stochastic variables X and Y, with distribution functions F<sub>X </sub>and F<sub>Y</sub>, the measure</p><p>χ = lim<sub>u</sub><sub>→1 </sub>P(F<sub>X</sub>(X) > u Ι F<sub>Y</sub>(Y) > u)</p><p>describes the pairwise extremal dependence between X and Y. If χ = 0, then the variables are <em>asymptotically independent</em>. If χ > 0, they are<br><em>asymptotically dependent</em>. Thus, extremes tend to occur simultaneously in space for processes that are asymptotically dependent, while this seldom occurs for asymptotically independent processes. It is a general belief that extreme precipitation tends to be asymptotically independent. However, to our knowledge, not much work has been put into analysing the extremal dependence structure of precipitation. Different statistical models have been developed, which can be applied for modelling spatial extremes. The most popular model is the <em>max-stable process</em>. Unfortunately, this model does not provide a good fit to asymptotically independent processes. Other models have been developed for better incorporating asymptotic independence, but most have not been extensively applied yet. We aim to examine the extremal dependence structure of precipitation in Norway, with the ultimate goal of modelling and simulating extreme precipitation. This is achieved by examining multiple popular statistics for extremal dependence, as well as comparing different spatial EVT models. This analysis is performed on hourly, gridded precipitation data from the MetCoOp Ensemble Prediction System (MEPS), which is publicly available from the internet: http://thredds.met.no/thredds/catalog/meps25epsarchive/catalog.html.</p>

Spatial conditional extremes via the Gibbs sampler.

2020 ◽  
Author(s):  
Adrian Casey ◽  
Ioannis Papastathopoulos
Keyword(s):  
Gibbs Sampler ◽  
Goodness Of Fit ◽  
Value Theory ◽  
New Method ◽  
Main Element ◽  
Asymptotic Independence ◽  
Random Component ◽  
Data Set ◽  
Spatial Extremes ◽  
Conditional Extremes

<div> <div> <div> <p>Spatial conditional extremes via the Gibbs sampler.</p> <p>Adrian Casey, University of Edinburgh</p> <p>January 14, 2020</p> <p>Conditional extreme value theory has been successfully applied to spatial extremes problems. In this statistical method, data from observation sites are modelled as appropriate asymptotic characterisations of random vectors <strong>X,</strong> conditioned on one of their components being extreme. The method is generic and applies to a broad range of dependence structures including asymptotic dependence and asymptotic independence. However, one issue that affects the conditional extremes method is the necessity to model and fit a multi-dimensional residual distribution; this can be challenging in spatial problems with a large number of sites.</p> <p>We describe early-stage work that takes a local approach to spatial extremes; this approach explores lower dimensional structures that are based on asymptotic representations of Markov random fields. The main element of this new method is a model for the behaviour of a random component X<sub>i </sub> given that its nearest neighbours exceed a sufficiently large threshold. When combined with a model for the case where the neighbours are below this threshold, a Gibbs sampling scheme serves to induce a model for the full conditional extremes distribution by taking repeated samples from these local (univariate) distributions.</p> <p>The new method is demonstrated on a data set of significant wave heights from the North Sea basin. Markov chain Monte-Carlo diagnostics and goodness-of-fit tests illustrate the performance of the method. The potential for extrapolation into the outer reaches of the conditional extreme tails is then examined.</p> <p>Joint work with Ioannis Papastathopoulos.</p> </div> </div> </div>

scholarly journals Verification and comparison of probabilistic precipitation forecasts using the TIGGE data in the upriver of Huaihe Basin

2011 ◽  
Vol 29 ◽  
pp. 95-102 ◽  
Author(s):  
L.-N. Zhao ◽  
F.-Y. Tian ◽  
H. Wu ◽  
D. Qi ◽  
J.-Y. Di ◽  
...  
Keyword(s):  
Gamma Distribution ◽  
Lead Time ◽  
The United States ◽  
Distribution Functions ◽  
Ensemble Prediction ◽  
Heavy Rainfall Event ◽  
Model Ensemble ◽  
China Meteorological Administration ◽  
Ensemble Prediction System ◽  
Prediction Systems

Abstract. The precipitation forecasts of three ensemble prediction systems (EPS) and two multi-model ensemble prediction systems (MM EPS) were assessed by comparing with observations from 19 rain gauge stations located in the Dapoling-Wangjiaba sub-catchment of Huaihe Basin for the period from 1 July to 6 August 2008. The sample Probabilistic Distribution Functions (PDF) of gamma distribution, the Relative Operating Characteristic (ROC) diagrams, the percentile precipitation and a heavy rainfall event are analyzed to evaluate the performances of the single and multi-model ensemble prediction system (EPS). The three EPS were from the China Meteorological Administration (CMA); the United States National Centre for Environment Predictions (NCEP); and the European Centre for Medium-Range Weather Forecasts (ECMWF), all were obtained from the TIGGE-CMA archiving centre (THORPEX Interactive Grand Global Ensemble, TIGGE). The MM EPS were created using the equal weighting method for every ensemble member over the test area, the first ( MM-1) consisted of all three EPS, the second (MM-2) consisted of the ECMWF and NCEP EPS. The results demonstrate the level of correspondence between deterioration in predictive skill and extended lead time. Compared with observations and with a lead time of one day, ECMWF performs a little better than other centre's. With over five days in advance, all the three EPS and the two MM EPS don't give reliable probabilistic precipitation forecasts. Both MM EPS can outperform CMA and NCEP for most of the forecasted days, but still perform a little worse than ECMWF. Though variation of daily percentile precipitation and ROC areas show MM-2 outperforms MM-1, gamma distribution indicates much similar performances for all 10-day forecast, and neither is superior to ECMWF.

scholarly journals Spatial extremes modeling applied to extreme precipitation data in the state of Paraná

2014 ◽  
Vol 11 (11) ◽  
pp. 12731-12764 ◽  
Author(s):  
R. A. Olinda ◽  
J. Blanchet ◽  
C. A. C. dos Santos ◽  
V. A. Ozaki ◽  
P. J. Ribeiro Jr.
Keyword(s):  
Probabilistic Models ◽  
Stable Process ◽  
Information Criterion ◽  
Value Theory ◽  
The State ◽  
Monthly Rainfall ◽  
Infinite Dimensions ◽  
Spatial Extremes ◽  
Historical Series ◽  
Active Research

Abstract. Most of the mathematical models developed for rare events are based on probabilistic models for extremes. Although the tools for statistical modeling of univariate and multivariate extremes are well developed, the extension of these tools to model spatial extremes includes an area of very active research nowadays. A natural approach to such a modeling is the theory of extreme spatial and the max-stable process, characterized by the extension of infinite dimensions of multivariate extreme value theory, and making it possible then to incorporate the existing correlation functions in geostatistics and therefore verify the extremal dependence by means of the extreme coefficient and the Madogram. This work describes the application of such processes in modeling the spatial maximum dependence of maximum monthly rainfall from the state of Paraná, based on historical series observed in weather stations. The proposed models consider the Euclidean space and a transformation referred to as space weather, which may explain the presence of directional effects resulting from synoptic weather patterns. This method is based on the theorem proposed for de Haan and on the models of Smith and Schlather. The isotropic and anisotropic behavior of these models is also verified via Monte Carlo simulation. Estimates are made through pairwise likelihood maximum and the models are compared using the Takeuchi Information Criterion. By modeling the dependence of spatial maxima, applied to maximum monthly rainfall data from the state of Paraná, it was possible to identify directional effects resulting from meteorological phenomena, which, in turn, are important for proper management of risks and environmental disasters in countries with its economy heavily dependent on agribusiness.

Study of perturbing method in regional BGM ensemble prediction system

2012 ◽  
Vol 4 (1) ◽  
pp. 65
Author(s):  
Xiao Yu-Hua ◽  
He Guang-Bi ◽  
Chen Jing ◽  
Deng Guo
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

scholarly journals Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

2019 ◽  
Vol 148 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Kevin Bachmann ◽  
Christian Keil ◽  
George C. Craig ◽  
Martin Weissmann ◽  
Christian A. Welzbacher
Keyword(s):  
Data Assimilation ◽  
Deep Convection ◽  
Radar Data ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Beneficial Impact ◽  
The Impact ◽  
Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

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